Experimental data is[1] clearly the lifeblood of any new technology. Getting data to prove out an invention can be the key to obtaining an important patent, generating early stage investment, and securing key partnerships. Earlier postings established the links between experimental data and creative process engineering as well as robust, useful models. However, generating data is expensive and time consuming, particularly as scale increases, making it critical to ensure that the right data is generated to make the best use of available resources.
I like to start by looking at the scale-up effort as one integrated data gathering exercise, with the overall goal of generating the necessary data to define the commercial process design. Along the way data is also needed to demonstrate a reduction in technical risk and allow optimization of the process economics. This is a bit of a different mindset from trying to prove out a ‘result’ at each scale (e.g. proving conversion of raw materials A and B into product C with desired efficiency X in the lab, then the lab-pilot, then the pilot, and finally the demo). So rather than charging ahead in result proving mode, some up front planning can ensure that the right data is gathered. After all, all data are equal, but some are more equal than others (with apologies to George Orwell…) [2]
This planning effort will yield a scale-up plan with experiments designed to generate the necessary design data and identify the parameters that have the greatest impact on economics and technical risk. In fact, the product of this effort is data, more than a physical fuel, chemical, or nutrition product.
A key part of this early stage planning is decoupling these parameters, understanding that ‘science parameters’ such as reaction kinetics and separation factors can, and should, be explored at the lab stage. Conversely, a lab scale effort to evaluate issues related to heat and mass transfer or pressure drop will be a futile effort at best leading to inconclusive or even incorrect results and is best done at a larger scale. This decoupling is illustrated in the following table:
Multi-scale data is beneficial for many additional reasons:
· Model development. Data at multiple scales enables generation of robust models for process development and equipment design.
· Troubleshooting. The smaller lab and pilot rigs can be instrumental to troubleshooting challenges in the larger units. If possible, it is worth the investment in to keep these smaller units operating in support of the larger scale operations.
· Continuous improvement. Continuous improvement is often needed while scaling a new technology to meet aggressive timelines and cost targets. These improvements can be identified and scaled in parallel to ensure that the first commercial unit has the benefit of the learnings from several generations of technology improvements that are identified and de-risked in multiscale operations.
By bringing Experimental Data together with Modeling and Analysis and Creative Process Engineering we develop a process concept, and an overall approach to reduce the time, cost, and risk of scale-up.
[1] I used to make sure I strictly used ‘data’ as a plural noun as the OED intended, but decided a while ago that this is somewhat cumbersome, and perhaps even a bit pretentious. I don’t think I am alone in this shift but am not sure the official definitions have caught up yet.
[2] Original Quote: “All animals are equal, but some are more equal than others”, George Orwell, Animal Farm